Decision Tree

Boosting

An ensemble technique that sequentially trains models, each focusing on correcting the errors of previous models.

Random Forest

An ensemble learning method that constructs multiple decision trees during training and outputs the mode of their predictions.

Gradient Boosting

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

XGBoost

An optimised distributed gradient boosting library designed for speed and performance in machine learning competitions and production.

Support Vector Machine

A supervised learning algorithm that finds the optimal hyperplane to separate different classes in high-dimensional space.

K-Nearest Neighbours

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

Naive Bayes

A probabilistic classifier based on applying Bayes' theorem with the assumption of independence between features.

Linear Regression

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

Logistic Regression

A classification algorithm that models the probability of a binary outcome using a logistic function.

Polynomial Regression

A form of regression analysis where the relationship between variables is modelled as an nth degree polynomial.

Tabular Deep Learning

The application of deep neural networks to structured tabular datasets, competing with traditional methods like gradient boosting through specialised architectures and regularisation.

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.

An online learning algorithm that balances exploration of new options with exploitation of known good options to maximise reward.

Techniques that artificially increase the size and diversity of training data through transformations like rotation, flipping, and cropping.

A game-theoretic approach to explaining individual model predictions by computing each feature's marginal contribution, based on Shapley values from cooperative game theory.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.